论文标题
通过在哈密顿变异的Ansatz中的平滑溶液转移避免贫瘠的高原
Avoiding barren plateaus via transferability of smooth solutions in Hamiltonian Variational Ansatz
论文作者
论文摘要
一项庞大的正在进行的研究工作重点是变分量子算法（VQAS），代表了领先的候选人，以实现当前量子设备的计算加速。除了古典计算机的模拟功能之外，VQA对大量量子的可伸缩性仍在争论中。两个主要障碍是低质量变异局部最小值的扩散，以及成本函数景观中梯度的指数消失，这一现象称为贫瘠的高原。在这里，我们表明，通过采用迭代搜索方案，人们可以有效地准备范式量子多体模型的基态，从而规避贫瘠的高原现象。这是通过利用向迭代解决方案的较大系统尺寸的转移性来完成的，显示了变分参数的固有平滑度，该结果无法扩展到通过随机启动的局部优化发现的其他解决方案。我们的方案可以直接在近期量子设备上进行测试，并在有利的局部景观中进行改进优化，并具有非呈现梯度。
A large ongoing research effort focuses on Variational Quantum Algorithms (VQAs), representing leading candidates to achieve computational speed-ups on current quantum devices. The scalability of VQAs to a large number of qubits, beyond the simulation capabilities of classical computers, is still debated. Two major hurdles are the proliferation of low-quality variational local minima, and the exponential vanishing of gradients in the cost function landscape, a phenomenon referred to as barren plateaus. Here we show that by employing iterative search schemes one can effectively prepare the ground state of paradigmatic quantum many-body models, circumventing also the barren plateau phenomenon. This is accomplished by leveraging the transferability to larger system sizes of iterative solutions, displaying an intrinsic smoothness of the variational parameters, a result that does not extend to other solutions found via random-start local optimization. Our scheme could be directly tested on near-term quantum devices, running a refinement optimization in a favorable local landscape with non-vanishing gradients.
